List, a CEA Tech institute, is speeding up neural network execution. The networks—inspired by biological neurons—are driving a new generation of products at GlobalSensing Technologies, a Dijon, France-based startup.

Neural networks are playing an increasing role in technologies to recognize images, sounds, or any other signal generated by a sensor or network of sensors—giving the devices the ability to interpret what they perceive. GlobalSensing Technologies uses neural networks in the artificial intelligence inside its embedded products. With applications in industries like food, video surveillance, and manufacturing, there are virtually no limits to the uses of neural networks, which help "recognize" specific environments or situations and trigger an appropriate response.

GlobalSensing Technologies has set up a joint lab with List, a member of the Instituts Carnot network, that has developed a processor whose architecture speeds up neural network execution. "The calculations required for this type of technology are simple, but great in number. The architecture we developed has the smallest, most efficient calculation units possible," said a List representative.How the data gets to the calculation units is also vital. The lab tested the architecture on a field-programmable logic gate array, which enables the rapid prototyping of a complete system.

The results are very promising, with 96% recognition rates—excellent for a very simple neural network—and a 60% improvement in energy efficiency over competing solutions if the application is ported to a dedicated FSOI* component.Several patents have been obtained for the new architecture, giving GlobalSensing Technologies a crucial competitive advantage on a high-growth market.

*Fully-depleted silicon-on-insulator: an innovative transistor technology with a thin insulating layer and a thin silicon film developed by Leti, a CEA Tech institute.

Researchers at List, a CEA Tech institute, have developed a technique for distributing software across machines in complex systems. This novel technique is faster and more efficient than the traditional method.

Surprisingly, one of the challenges of complex systems—made up of a large number of software applications that interact locally and simultaneously—is the long, laborious task of distributing the software across the available computing resources. List researchers have solved this problem, with a simple, time-saving approach. In research carried out in partnership with ENS Cachan and CRAN (Centre de Recherche en Automatique de Nancy), List researchers leveraged the institute's Papyrus model-based engineering studio to digitize the many command-control functions of a nuclear power plant.

Once the software bricks were ready, they had to be distributed across the available servers depending on how critical the function they determine is and the capacities of each computer. This "function allocation" step can be carried out by optimization software, which tests all of the possible solutions one by one. However, in systems of systems, this approach is too complex and, therefore, costly in terms of both human resources and time. To solve this problem, the researchers used List's Diversity algorithm validation platform to find a solution that would respond to all of the specified requirements, but without seeking optimization at all cost, "A concession that considerably reduced computation time without compromising on security or reliability."

The approach has been validated for use in the nuclear industry and could now be used to optimize complex systems in general, and, specifically, in advanced manufacturing to distribute the production of a heterogeneous set of products across several factories, for example.